Introduction: One of the challenges in developing Computer-Aided Diagnosis (CAD) systems is their accurate and comprehensive assessment. This paper presents the conduction and results of a systematic review (SR) that aims to verify the state of the art regarding the assessment of CAD systems. This survey provides a general analysis of the current status of the design, development and assessment of such systems and includes discussions on the most used metrics and approaches that could be utilized to obtain more objective evaluation methods. Methods: The SR was conducted using the scientifi c databases, ACM Digital Library, IEEE Xplore Digital Library, ScienceDirect and Web of Science. Inclusion and exclusion criteria were defi ned and applied to each retrieved work to select those of interest. From 156 studies retrieved, 100 studies were included. Results: There is a number of abnormalities that have been used for the development of CAD systems. Images from computed tomographies and mammographies are the most encountered types of medical images. Additionally, a number of studies used public databases for CAD evaluations. The main evaluation metrics and methods applied to CAD systems include sensitivity, accuracy, specifi city and receiver operating characteristic (ROC) analyses. In the assessed CAD systems that used the segmentation method, 30.0% applied the overlap measure. Discussion: There remain several topics to explore for the assessment of CAD schemes. While some evaluation metrics are traditionally used, they require a prior knowledge of case characteristics to test CAD systems. We were not able to identify articles that use software testing to evaluate CAD systems. Thus, we realize that there is a gap between CAD assessments and traditional practices of software engineering. However, the scope of this research is limited to scientifi c and academic works and excludes commercial interests. Finally, we discuss potential research studies within this scope to create a more objective and effi cient evaluation of CAD systems.

Content-Based Image Retrieval (CBIR) systems constitute an innovative approach to store, to compare and to query images in a database. Visual aspects such as color, texture or shape are used to perform such operations. Recently, CBIR concepts were applied to build testing oracles for image processing programs, where test verdicts (approval/disapproval) are based on similarity measures between images produced by the program and reference images. However, the results of a CBIR system may vary depending on the components employed in the system (feature extractors and similarity functions), and few studies assessing this influence have been found in the literature. Our aim is to present an empirical analysis of ten similarity functions in CBIR systems within the context of software testing with graphic outputs. A case study with images obtained from a computer-aided diagnosis system in mammography indicated some variability among image test verdicts (approval/disapproval) according to the similarity function choice. The case study also indicates the existence of some clusters of similarity functions with high correlation coefficients.

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AGRADECIMENTOSAgradeço, primeiramente, a Deus por me inspirar e dar forças para chegar até aqui, até mesmo quando eu acreditei que não conseguiria.Agradeçoà Nossa Senhora que, com seu exemplo de humildade e fidelidade, me inspirou a prosseguir na caminhada, mesmo diante das dificuldades.Agradeçoà minha mãe, Maria Aparecida Mendonça Gonçalves, exemplo de luta e perseverança, que não me viu concluir este trabalho, mas que teve, tem e sempre terá papel fundamental em cada passo que eu dou. Agradeçoà minha orientadora, Profa. Dra. Fátima de Lourdes dos Santos Nunes Marques, primeiramente por acreditar que eu conseguiria vencer esse desafio, mesmo quando o caminhar estava difícil e duvidoso. Agradeço também por todo o apoio e orientação, desde a graduação, que me auxiliaram a chegar até aqui. Agradeçoà minha namorada, Camila Ericka Andrade de Melo, por todo o amor, carinho e apoio incondicionais, bem como pelas palavras de conforto e motivação que tiveram papel fundamental na minha caminhada. Agradeço ao meu pai, João Costa Gonçalves, e ao meu irmão, Carlos Junior Mendonça Gonçalves, que estão sempre ao meu lado, partilhando os bons momentos e também os difíceis. Agradeço aos colegas Rafael Alves Paes de Oliveira (LabES/ICMC-USP) e Leila Cristina Carneiro Bergamasco (LApIS/EACH-USP) pelo precioso apoio que me concederam durante a condução deste trabalho. Agradeço ao Prof. Dr. Márcio Eduardo Delamaro (LabES/ICMC-USP) pelo precioso apoio e parceria na condução dos projetos de pesquisa que me trouxeram até aqui. Agradeçoà minha tia, Ana Cristina da Silva Leão, por todo o apoio e amizade que tem dedicadoà minha família, em especial, nosúltimos anos; sem eles, com certeza, a caminhada até aqui seria muito mais difícil. A todos o meu muito obrigado! Palavras-chave: Recuperação de vídeos baseada em conteúdo. CBVR. Diagnóstico auxiliado por computador. Vídeos médicos. Imagens médicas. ABSTRACT GONÇ ALVES, Vagner Mendonça. Content-based medical video retrieval using visual and sound feature extractors. 2017. 99 p. Dissertation (Master of Science) -School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, 2016. Corrected version.Advance of storage devices and computer networks has contributed to digital videos assume an important role in the development of multimedia information systems. In order to take advantage of the full potential of digital videos in the development of these systems, it is necessary the development of efficient techniques for automated data analysis, interpretation and retrieval. Content-based video retrieval (CBVR) allows processing and analysis of content in digital videos to extract relevant information and enable indexing and retrieval. Scientific studies have proposed the application of CBVR in medical video databases in order to provide different contributions like computer-aided diagnosis, decisionmaking support or availability of video databases for use in medical training and education. In general, visual characteristics are the main information used in the context of CBVR appl...

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